Masked Visual Pre-training for Motor Control (2203.06173v1)

Abstract: This paper shows that self-supervised visual pre-training from real-world images is effective for learning motor control tasks from pixels. We first train the visual representations by masked modeling of natural images. We then freeze the visual encoder and train neural network controllers on top with reinforcement learning. We do not perform any task-specific fine-tuning of the encoder; the same visual representations are used for all motor control tasks. To the best of our knowledge, this is the first self-supervised model to exploit real-world images at scale for motor control. To accelerate progress in learning from pixels, we contribute a benchmark suite of hand-designed tasks varying in movements, scenes, and robots. Without relying on labels, state-estimation, or expert demonstrations, we consistently outperform supervised encoders by up to 80% absolute success rate, sometimes even matching the oracle state performance. We also find that in-the-wild images, e.g., from YouTube or Egocentric videos, lead to better visual representations for various manipulation tasks than ImageNet images.

• The paper introduces MVP, which pre-trains a Vision Transformer using masked autoencoders to extract key features from large-scale, real-world image datasets.
• The approach decouples visual representation learning from control policy training, achieving up to 80% higher success rates than supervised baselines.
• The study establishes the PixMC benchmark to validate robust performance and generalizability across diverse robotic manipulation tasks.

Analyzing "Masked Visual Pre-training for Motor Control"

The paper under consideration proposes a novel approach to enhance the learning of motor control tasks from pixel data using self-supervised visual pre-training. The primary contribution lies in harnessing large-scale, real-world image datasets to pre-train visual representations, which are subsequently leveraged to train reinforcement learning (RL) controllers across various motor control tasks. This method eliminates the need for task-specific fine-tuning of the visual encoder, presenting notable benefits in terms of generalizability and resource efficiency.

Key Contributions and Methodology

The authors introduce Masked Visual Pre-training for Motor Control (MVP), which pre-trains a Vision Transformer (ViT) encoder using a masked autoencoder (MAE) model. The pre-training leverages large collections of real-world images, specifically using a Human-Object Interaction dataset (HOI) composed of egocentric video frames, thereby emphasizing visual representations tailored to object manipulation tasks. This approach diverges from conventional ImageNet-based transfer learning by focusing on representations that capture relevant features for manipulative actions, showing superior performance in this domain.

After pre-training, the visual encoder is frozen, providing input to an RL algorithm that learns task-specific motor control policies. This separation of visual and control learning tasks demonstrates a higher sample efficiency, significantly outperforming traditional supervised visual encoders by up to 80% in absolute success rates, and in some scenarios, matching oracle state performance.

Benchmark Suite and Experimental Results

A new benchmark suite, PixMC, is introduced to evaluate the proposed method. It encompasses various tasks with multiple robot configurations and provides a rigorous platform to assess the efficacy of visual representation learning in the context of motor control. This benchmark leverages the capabilities of the NVIDIA IsaacGym simulator to provide fast, GPU-based physics simulation conducive to RL tasks.

The experiments reveal that MVP outperforms supervised baseline methods consistently across tasks and demonstrates robustness to distractors like varying object colors and shapes, maintaining high success rates. Notably, the representations learned from in-the-wild datasets such as YouTube or Egocentric videos exhibit superior performance over those trained using ImageNet, indicating the value of domain-specific visual features for motor control tasks.

Implications and Future Directions

The research presents several implications for designing RL systems for motor control. By decoupling visual encoder training from control policy learning, the approach offers a scalable and efficient framework suitable for complex robotic manipulation tasks. The findings suggest that leveraging large, diverse datasets for self-supervised learning can lead to robust and transferable visual representations, enhancing the generalization capabilities of robotic systems.

The introduction of the PixMC benchmark provides a valuable tool for further exploring the intersection of computer vision and robotics, paving the way for more sophisticated and adaptable robotic control systems.

Future work might explore scaling up both data and model size, or experimenting with other self-supervised frameworks such as contrastive learning to further improve representation quality. Additionally, investigating the potential of end-to-end training with larger environments to overcome the observed challenges could refine and expand the applicability of self-supervised learning approaches in robotics.

In conclusion, the paper makes a significant contribution to the field of motor control by demonstrating the utility of self-supervised visual pre-training, fostering progress toward more adaptive and efficient robotic systems.